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カワイ ユキコ
KAWAI YUKIKO
河合 由起子 所属 京都産業大学 情報理工学部 情報理工学科 職種 教授 |
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| 言語種別 | 英語 |
| 発行・発表の年月 | 2017 |
| 形態種別 | 研究論文（国際会議プロシーディングス） |
| 査読 | 査読あり |
| 標題 | Visualization of spatio-temporal events in geo-tagged social media |
| 執筆形態 | その他 |
| 掲載誌名 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| 出版社・発行元 | Springer Verlag |
| 巻・号・頁 | 10181,pp.137-152 |
| 著者・共著者 | Yuanyuan Wang,Muhammad Syafiq Mohd Pozi,Goki Yasui,Yukiko Kawai,Kazutoshi Sumiya,Toyokazu Akiyama |
| 概要 | This paper presents a spatio-temporal mapping system for visualizing a summary of geo-tagged social media as tags in a cloud, and it is associated with a web page by detecting spatio-temporal events. Through it, users can grasp events at anytime from anywhere while they browse any web pages. In order to detect spatio-temporal events from social media such as tweets, the system extracts expected events (e.g., crowded restaurants) by using machine learning algorithms to classify tweets through space and time, and it also extracts unexpected or seasonal events (e.g., time sales) by comparing the current situation to those normal regularities. Thus, the system presents a social tag cloud of tweets to help users gain a quick overview of spatio-temporal events while they browse a web page, and it also presents a tweet list to help users obtain more details about events. Furthermore, users can freely specify a time period or a tag to view its related tweets. Finally, we discuss our proposed social tag cloud generation method’s effectiveness using dense geo-tagged tweets at multi-functional buildings in urban areas. |
| DOI | 10.1007/978-3-319-55998-8_9 |
| ISSN | 1611-3349 |
| DBLP ID | conf/w2gis/WangPYKSA17 |
| PermalinkURL | http://dblp.uni-trier.de/db/conf/w2gis/w2gis2017.html#conf/w2gis/WangPYKSA17 |